I recently spent two valuable days at the Freeslate Forum in San Francisco. Two of the case studies were on the use of high throughput tools in more academic settings, by Emory Chan of The Molecular Foundry at Lawrence Berkeley National Laboratory and by Scott Virgil at the Center for Catalysis and Chemical Synthesis (3CS)… Read More »
Step 7. We ran the experiment… Now what? We have data, we need knowledge. What we mean by information — the elementary unit of information — is a difference which makes a difference. (Gregory Bateson) At the end of step 6, we had an experimental plan that the entire team could understand and that was… Read More »
Landscapes with moderate degrees of ruggedness share a striking feature: it is the highest peaks that can be scaled from the greatest number of initial positions! (Stuart Kauffman, At Home in the Universe) In the previous step, we quickly found that even with a relatively modest selection of factors and levels, the combinatoric explosion can… Read More »
“To boldly go where no one has gone before…” (Star Trek) If a project is worth the investment of a high throughput laboratory, it must have an important element of discovery, pushing away from the familiar into different spaces. But the simple facts of chemistry generate enormous numbers of combinations that quickly become a nightmare… Read More »
Step 2. What are we measuring? Are we measuring it well? Not everything that can be counted counts and not everything that counts can be counted. (Albert Einstein) In the previous step, we discussed ways to move from the goals and objectives of the business to unbiased, specific and measurable laboratory objectives. Now we need… Read More »
Step 6. How do we actually get the data? At the end of Section 5 we have an array of points that statistically should produce data adequate to generating a useful result. However, “Designing an experiment is like gambling with the Devil…” (Sir R. A. Fisher). The ever-present noise of our equipment, laboratory, building, city,… Read More »
Step 1. What are we doing? How do we move forward? High throughput experimentation is qualitatively different from standard laboratory work. The simple fact of 10-100x speedup changes the game as much as switching from propellers to jets changed the airline industry. This must be reflected in the laboratory strategy. Nevertheless, the basic development of… Read More »
Step 5. What do we do in the laboratory? “You can’t fix by analysis what you bungled by design.” – By Design (Light, Singer and Willett) This is the part of the experimental strategy process where most Design of Experiments (DOE) classes start. The student is taught about Analysis of Variance (ANOVA), factorial designs, and… Read More »
Step 4. Do we have the resources? “The amateurs discuss tactics: the professionals discuss logistics.” – Napoleon Bonaparte It is really frustrating to get half way through an experiment and then get pulled off for “higher priority” work. If that happens during a designed experiment, the return for your effort could be ZERO! In this… Read More »
Step 3. How to vary the right factors – by the right amounts To find out what happens when you change something, it is necessary to change it. (Box, Hunter, & Hunter) Have you ever looked at an experiment and wondered how you could possibly decide which factors to test? In this post you’ll learn… Read More »